{"id":"https://openalex.org/W4304091694","doi":"https://doi.org/10.1145/3503161.3547959","title":"Towards High-Fidelity Face Normal Estimation","display_name":"Towards High-Fidelity Face Normal Estimation","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4304091694","doi":"https://doi.org/10.1145/3503161.3547959"},"language":"en","primary_location":{"id":"doi:10.1145/3503161.3547959","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547959","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100377121","display_name":"Meng Wang","orcid":"https://orcid.org/0000-0002-0001-2074"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Meng Wang","raw_affiliation_strings":["Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101701657","display_name":"Chaoyue Wang","orcid":"https://orcid.org/0000-0002-9002-1029"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaoyue Wang","raw_affiliation_strings":["JD Explore Academy, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD Explore Academy, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090356888","display_name":"Xiaojie Guo","orcid":"https://orcid.org/0000-0002-0326-8382"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojie Guo","raw_affiliation_strings":["Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086311840","display_name":"Jiawan Zhang","orcid":"https://orcid.org/0000-0002-0667-6744"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawan Zhang","raw_affiliation_strings":["Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100377121"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09403964,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"37","issue":null,"first_page":"5172","last_page":"5180"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8259224891662598},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.6277158856391907},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.6012890338897705},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5994411706924438},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5598903298377991},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5176215767860413},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4905051589012146},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.46292993426322937},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4353238046169281},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.4262136220932007},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.42009931802749634},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4133719205856323},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39649415016174316},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34806302189826965},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09762915968894958}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8259224891662598},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.6277158856391907},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.6012890338897705},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5994411706924438},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5598903298377991},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5176215767860413},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4905051589012146},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.46292993426322937},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4353238046169281},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.4262136220932007},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.42009931802749634},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4133719205856323},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39649415016174316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34806302189826965},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09762915968894958},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3503161.3547959","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547959","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7757445241","display_name":null,"funder_award_id":"2019YFC1521200","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G951518816","display_name":null,"funder_award_id":"62172295, 62072327","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1834627138","https://openalex.org/W1930824406","https://openalex.org/W2026551261","https://openalex.org/W2027560260","https://openalex.org/W2035683503","https://openalex.org/W2038294257","https://openalex.org/W2058961190","https://openalex.org/W2087007396","https://openalex.org/W2100810849","https://openalex.org/W2143915994","https://openalex.org/W2165916500","https://openalex.org/W2295537950","https://openalex.org/W2475287302","https://openalex.org/W2584229793","https://openalex.org/W2599226450","https://openalex.org/W2603777577","https://openalex.org/W2604721644","https://openalex.org/W2604880013","https://openalex.org/W2738588019","https://openalex.org/W2767225062","https://openalex.org/W2768508823","https://openalex.org/W2771376539","https://openalex.org/W2795709097","https://openalex.org/W2796822548","https://openalex.org/W2809852002","https://openalex.org/W2889050557","https://openalex.org/W2890264026","https://openalex.org/W2922521335","https://openalex.org/W2955625300","https://openalex.org/W2962770929","https://openalex.org/W2962780596","https://openalex.org/W2962809185","https://openalex.org/W2963073614","https://openalex.org/W2963342110","https://openalex.org/W2963420272","https://openalex.org/W2963557052","https://openalex.org/W2979841973","https://openalex.org/W3034723751","https://openalex.org/W3035574324","https://openalex.org/W3109016549","https://openalex.org/W3111002277","https://openalex.org/W3167101089","https://openalex.org/W3179645636","https://openalex.org/W3180195040","https://openalex.org/W3216722857","https://openalex.org/W4249033934","https://openalex.org/W4300424419","https://openalex.org/W6752851532","https://openalex.org/W6754208138"],"related_works":["https://openalex.org/W2381850946","https://openalex.org/W4380449851","https://openalex.org/W3125091513","https://openalex.org/W4318832338","https://openalex.org/W4295532600","https://openalex.org/W2063823869","https://openalex.org/W1919390113","https://openalex.org/W4401571341","https://openalex.org/W1552490587","https://openalex.org/W3035701170"],"abstract_inverted_index":{"While":[0],"existing":[1],"face":[2,23,29,35,50,69,86,116,142,208],"normal":[3,30,36,51,70,87,112,124,132,170],"estimation":[4,37,133],"methods":[5,66,195],"have":[6],"produced":[7],"promising":[8],"results":[9],"on":[10,20,166],"small":[11],"datasets,":[12],"they":[13],"often":[14],"suffer":[15],"from":[16,67,88,139],"severe":[17],"performance":[18],"degradation":[19],"diverse":[21],"in-the-wild":[22,90],"images,":[24],"especially":[25],"for":[26],"the":[27,110,121,140,145,156,168,184],"high-fidelity":[28,34,57,169],"estimation.":[31],"Training":[32],"a":[33,43,81,94,102,114],"model":[38,103],"with":[39,49,71,92],"generalization":[40],"capability":[41],"requires":[42],"large":[44],"amount":[45],"of":[46,113,186,198,206],"training":[47,106,162,200],"data":[48,107,201],"ground":[52],"truth.":[53],"Since":[54],"collecting":[55],"such":[56],"database":[58],"is":[59,212],"difficult":[60],"in":[61,172,196],"practice,":[62],"which":[63],"prevents":[64],"current":[65],"recovering":[68],"fine-grained":[72,146,207],"geometric":[73],"details.":[74],"To":[75],"mitigate":[76],"this":[77,149],"issue,":[78],"we":[79,99,119],"propose":[80],"coarse-to-fine":[82],"framework":[83],"to":[84,108,135,144,182],"estimate":[85],"an":[89,126,130],"image":[91,143],"only":[93],"coarse":[95,111,123],"exemplar":[96,127],"reference.":[97],"Specifically,":[98],"first":[100],"train":[101],"using":[104],"limited":[105],"exploit":[109],"real":[115],"image.":[117],"Then,":[118],"leverage":[120],"estimated":[122],"as":[125],"and":[128,164,177,189,203],"devise":[129],"exemplar-based":[131],"network":[134],"explore":[136],"robust":[137],"mapping":[138],"input":[141],"normal.":[147,209],"In":[148],"manner,":[150],"our":[151,187],"method":[152],"can":[153],"largely":[154],"alleviate":[155],"negative":[157],"impact":[158],"caused":[159],"by":[160],"lacking":[161],"data,":[163],"focus":[165],"exploring":[167],"contained":[171],"natural":[173],"images.":[174],"Extensive":[175],"experiments":[176],"ablation":[178],"studies":[179],"are":[180],"conducted":[181],"demonstrate":[183],"efficacy":[185],"design,":[188],"reveal":[190],"its":[191],"superiority":[192],"over":[193],"state-of-the-art":[194],"terms":[197],"both":[199],"requirement":[202],"recovery":[204],"quality":[205],"Our":[210],"code":[211],"available":[213],"at":[214],"\\urlhttps://github.com/AutoHDR/HFFNE.":[215]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
